Digital Image Processing ECE 178 Winter On the WEB. Class list/discussion sessions. Today: Jan About this course.

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1 Digital Image Processing ECE 178 Winter 2003 On the WEB For course information and slides and more: B. S. MANJUNATH RM 3157 ENGR I Tel: manj@ece.ucsb.edu 1/07/2003 W03/Lecture 1 1 Teaching Assistants Jelena Tesic (jelena@ece.ucsb.edu) Marco Zuliani (zuliani@ece.ucsb.edu) 1/07/2003 W03/Lecture 1 2 Class list/discussion sessions Today: Jan ALL Registered students: please send an to zuliani@ece.ucsb.edu today. subject = ECE 178 with your name (last, first) address (if different) If you have any schedule conflicts for either of the two discussion sessions ( or PM) on Fridays. Your address is needed to send course related information/announcements. Course outline Requirements for the course Introduction to image processing Matlab basics and the image processing toolbox 1/07/2003 W03/Lecture 1 3 1/07/2003 W03/Lecture 1 4 About this course Grading Prerequisites Strong motivation, basic calculus MATLAB is the programming environment, but no prior background in MATLAB is assumed. Who can take this course? Juniors/Seniors/Graduate students in ECE/CE/CS/ME/MATP/ Text Book: Gonzalez and Woods, 2 nd Edition (2002) 1/07/2003 W03/Lecture 1 5 H/W /Comp* 20% due by 11:59pm on the due date Project 20% Midterms 20% (two mid-terms) Finals 40% * All homeworks are required. A non-submission will affect your grade non-linearly. 1/07/2003 W03/Lecture 1 6

2 Important Dates Why Image Processing? Mid-term I: Tuesday, February 4, Mid-Term II: Tuesday, February 25 (tentative) Final Examination: Monday, March 17, 12-3pm (as per schedule) The future is multimedia information processing Images (and video) are everywhere! Many and diverse applications Astronomy, biology, geology, geography, medicine, law enforcement, defense, Industrial inspection, Different imaging modalities: visual, X-ray, ultrasound, 1/07/2003 W03/Lecture 1 7 1/07/2003 W03/Lecture 1 8 Entertainment Security Digital camcorders HDTV DVDs: High quality image/video compression (MPEG-2: about 5-10 Million bits/second) Digital Cinema New compression technologies are needed Consider a 2 hour movie: 1920 x 1080 x 30 bits/pixel x 24 frames/second ~~ 1.5 billion bits/second! 1.3 terra bytes / 2 hr program 1/07/2003 W03/Lecture 1 9 Person Identification Face recognition Finger print identification Watermarking Copyright protection and authentication Data hiding Secret communication (Steganography) 1/07/2003 W03/Lecture 1 10 Some Applications X-ray imaging and radiology Computer Tomography An Ultrasound image Profile of a fetus at four months. This face is approximately 1 ½ inches (4cm) long. ( [545x bit color JPEG, bytes] Section through Visible Human Male - head, including cerebellum, cerebral cortex, brainstem, nasal passages (from Head subset) 1/07/2003 W03/Lecture /07/2003 W03/Lecture 1 12

3 Computer Tomography Image/video Processing Methods Generating 3-D images from 2-D slices. CAD, CAM applications Industrial inspections CT Scanner Picker PQ 6000 Model GE Medical High Speed Advantage scanner Picker PQ 6000 Image Enhancement Image Restoration Compression Image reconstruction Morphological image processing Feature extraction and recognition" computer vision 1/07/2003 W03/Lecture /07/2003 W03/Lecture 1 14 Chapter 1: Introduction Image Enhancement 1/07/2003 W03/Lecture 1 15 Enhancement: Improve the visual quality of the image. Eg. Noise removal using median filtering (from 1/07/2003 W03/Lecture 1 16 Image Restoration IP methods (cont.) same as image enhancement, but you have additional information concerning the quality degradation. Example: removing motion blur in an image of a fast moving object. A page from Matlab examples or the matlab site at magetlbx/examples/deblur/deblur.html 1/07/2003 W03/Lecture 1 17 Reconstruction: reconstruction from projections. Used in constructing 3D data from 2D projections in computer tomography. Image representation using features Low level representations using color, texture, shape, motion, etc. High level features for recognitions; e.g., facial features. Recognition and scene understanding 1/07/2003 W03/Lecture 1 18

4 Image Processing, Pattern Recognition, Graphics, and Computer Vision Course Outline Image Processing This is about image to image transformation (image coding, enhancement, restoration, etc.) ECE 178, ECE 278a. Computer Graphics: CS 180/280 Pattern Recognition: ECE 277b Computer Vision: ECE 181b/281b Multimedia computing: ECE 160 Introduction Chapters D Linear Systems Class notes; Sampling and Quantization Class notes; Ch 2.4 Image Enhancement Ch. 3, 4 Image and Video Coding Project presentations 1/07/2003 W03/Lecture /07/2003 W03/Lecture 1 20 Course Projects Image Compression using Wavelets Why project? To learn more about applications of image processing and get hands-on experience. typically, the material (needed) is NOT covered in class - thus requires independent study (ten weeks is too short to cover all interesting topics!.) Winter 2003: This quarter we will explore Image compression in more detail Wavelets based image compression (~JPEG2000) What are wavelets? (we will learn more about them later on..) Using wavelets for data compression JPEG 2000 standard is based on wavelets JPEG (original) is based on the Discrete Cosine Transform you will learn DCT based compression in our discussions on image coding. 1/07/2003 W03/Lecture /07/2003 W03/Lecture 1 22 Previous year (2002) projects Data Hiding JPEG 2000 Data hiding Streaming Video Image Mosaicing Droeshout engraving of William Shakespeare (192x240) Steganography is the art and science of communicating in a way which hides the existence of the communication. In contrast to cryptography, where the "enemy" is allowed to detect, intercept and modify messages without being able to violate certain security premises guaranteed by a cryptosystem, the goal of steganography is to hide messages inside other "harmless" messages in a way that does not allow any "enemy" to even detect that there is a second secret message present [Markus Kuhn ]. A text message (1535 bytes) 1/07/2003 W03/Lecture /07/2003 W03/Lecture 1 24

5 Results of Embedding Text Example: Image in Image Embedded image Compressed image (lossy JPEG 85%) Steganography is the art and science of communicating in a way which hides the existence of the communication. In contrast to cryptography, where the "enemy" is allowed to detect, intercept and modify messages without being able to violate certain security premises guaranteed by a cryptosystem, the goal of steganography is to hide messages inside other "harmless" messages in a way that does not allow any "enemy" to even detect that there is a second secret message present [Markus Kuhn ]. Renoir s Le Moulin de la Galette (432x320) Airphoto image (216x160) Recovered message (loss-less) 1/07/2003 W03/Lecture 1 25 Recovered signature image Embedded Embedded and JPEG compression (85%) 1/07/2003 W03/Lecture 1 26 Example: Video in Video Streaming video over wireless (a) Host frame (cm , 352x240) (b) Embedded frame (2M bps, 30 f/s PSNR 31.5dB) (c) Recovered frame (PSNR 35.7dB) Video is high bandwidth data Wireless, at present, has limited bandwidth Needs efficient and effective compression Experiment with new coding techniques such as MPEG-4 etc. (d) Signature frame (e) Recovered signature frame (cm , 352x240) (PSNR 45.0dB) 1/07/2003 W03/Lecture /07/2003 W03/Lecture 1 28 Image/Video Mosaicing What is mosaicing? Stitching together two or more images taken at different times or using different sensors, so as to create an image with larger viewing area. Video mosacing: stitching together video frames. General procedure Identify control points that are good for matching Match them, thus establishing a correspondence Matching is difficult! 1/07/2003 W03/Lecture 1 29 Image ComptessionProject: Timeline Plan in advance; you have only ten weeks!! Jan 16: Project details will be provided ( and on web) Jan 17: form groups-not exceeding 3/group and inform TA (zuliani@ece.ucsb.edu) If you need help in deciding, contact me. Week of Feb 17: Meet with instructor to discuss progress (individual groups). March 11: project presentations in class (10 mins each) March 14: Final project reports due. 1/07/2003 W03/Lecture 1 30

6 A note on human visual perception Brightness Adaptation Both the hardware and software of human visual perception are extremely complex and they work! A good understanding of the acquisition hardware (eyes) Very little known about higher level (perceptual) processing. The total range that our visual system can discriminate at a given time is rather small. Brightness adaptation refers to this ability to adjust its(eye) sensitivity over a wide range of adaptation levels. 1/07/2003 W03/Lecture /07/2003 W03/Lecture 1 32 Brightness Discrimination Weber Ration 1/07/2003 W03/Lecture /07/2003 W03/Lecture 1 34 Perceived Brightness Simultaneous Contrast 1/07/2003 W03/Lecture /07/2003 W03/Lecture 1 36

7 Optical Illusions 1/07/2003 W03/Lecture 1 37